D&A techniques powered by real-time traveler information can help public transport operators ease the ever-growing congestion.
As public transport operators struggle to match roadway and transit network capacity with rising demand, ground-breaking data and analytics (D&A) techniques - powered by real-time traveler information - can help ease the ever-growing congestion.
With transport volumes multiplying on roads and public transport systems, this congestion frustrates customers, increases the wear and tear on assets and slows the network. And the problem only compounds, as uneven traffic flows at peak times lead to over-usage on main routes and overcrowding on transit vehicles, causing more delays. In other words, congestion creates more congestion.
While advances in traveler communications – like trip planning websites, motorist satellite navigation (satnav) tools, email notifications and commuter apps – can alert people to hold-ups and suggest alternate routes, these messages may simply divert everyone in the same direction, creating new problems. At the same time, transport authorities may watch in frustration as some elements of the network are overcrowded, while others have spare capacity.
There are excellent examples of how transport authorities are making focused investments in technology to relieve heavily-overstretched infrastructure, improve journey times, travelers’ experiences and investment.
For example, transport authorities in Greater London are taking action in response to data that revealed the number of passenger journeys on Transport for London (TfL) services increased by half a billion in just five years1. As they considered the situation, they recognized that they possessed vast data, which could feed into sophisticated capacity and demand models, as well as the potential connectivity to get the right messages out there.
London’s TfL is now evaluating the best approaches to leverage its many established databases, like the Oyster transit-user card, transit system contactless payment systems and the Congestion Charge driver toll system. By doing so, they could access powerful assets, namely behavioral details for many travelers, to gain an understanding of their typical travel patterns.
TfL is beginning its own journey to invest in tools to analyze the data and tailor customer communications. For example, by collecting data about its customers, through their individual accounts and through multiple engagement channels, transport operators could segment those travelers experiencing congestion or delays and then suggest different ways to reach their destinations, and make best use of available capacity. Using emails, SMS and apps, operators could even offer passengers incentives to take a particular route, travel at a specific time, or use a certain mode. And with real-time data on recipients’ behavior, operators can quickly adjust their messages to focus on the most effective incentives.
TfL is also investigating the use of mobile phone network data to track increases in road traffic in real time2. This, along with the growth in ‘connected cars,’ which transmit data on their movements and satnav destination, will soon provide transport managers with enhanced tools to predict and immediately respond to the formation of traffic jams. They can then amend traffic light timing to ease congestion or even coordinate route directions among in-car navigation providers.
The need for richer data will also grow as transport planners explore the concept of Mobility as a Service (MaaS), by which traveler needs could shift from private ownership of vehicles to consumption of transportation as a service, through a mix of public and private transport modes that enable them to travel according to their time, comfort and cost preferences.
Knowing the purpose of a customer journey would make it easier to manage demand optimally and equitably, but it is a highly challenging issue. Short of asking all travelers to register normal journeys and modal shift preferences, there is a need for more granular but non-invasive mechanisms to determine journey purpose and respond with tailored options to deliver MaaS.
The ultimate solution would result in strategic management of demand across public and private transport. Then, for example, a city-bound driver heading down the highway towards a major traffic incident could be told how much time they’d save by stopping at the nearest commuter park & ride and taking a train or bus.
Ultimately, this combination of data, analytics and personalized messages could strengthen the ability to use infrastructure at close to its optimum load, taking full advantage of the system’s capacity, but avoiding the need for investments that only pay off at the busiest times.
As public authorities develop these systems, they will encounter hurdles around the technology, the data-gathering, the analytics techniques and the communications systems.
The biggest challenges are likely to lie in persuading and organizing people. For example, travelers will only listen to messages if they trust the source, if they’re confident that the organizations’ use of their personal data is both ethical and transparent, and that altering their route will produce the promised benefits. This requires good co-ordination among transport infrastructure managers and operators to manage the flows of data around the system.
If these systems are developed in the right way, travelers can route around congestion, and reduce the amount of congestion in the first place. This can provide citizens with better transport services and enable transport managers and infrastructure investors to maximize the capacity of our hard-pressed transport networks.